Epidemiology and Outcomes of Invasive Candidiasis Due to Non-albicans Species of Candida in 2,496 Patients: Data from the Prospective Antifungal Therapy (PATH) Registry 2004–2008
Bibliographic record
Abstract
This analysis describes the epidemiology and outcomes of invasive candidiasis caused by non-albicans species of Candida in patients enrolled in the Prospective Antifungal Therapy Alliance (PATH Alliance) registry from 2004 to 2008. A total of 2,496 patients with non-albicans species of Candida isolates were identified. The identified species were C. glabrata (46.4%), C. parapsilosis (24.7%), C. tropicalis (13.9%), C. krusei (5.5%), C. lusitaniae (1.6%), C. dubliniensis (1.5%) and C. guilliermondii (0.4%); 111 infections involved two or more species of Candida (4.4%). Non-albicans species accounted for more than 50% of all cases of invasive candidiasis in 15 of the 24 sites (62.5%) that contributed more than one case to the survey. Among solid organ transplant recipients, patients with non-transplant surgery, and patients with solid tumors, the most prevalent non-albicans species was C. glabrata at 63.7%, 48.0%, and 53.8%, respectively. In 1,883 patients receiving antifungal therapy on day 3, fluconazole (30.5%) and echinocandins (47.5%) were the most frequently administered monotherapies. Among the 15 reported species, 90-day survival was highest for patients infected with either C. parapsilosis (70.7%) or C. lusitaniae (74.5%) and lowest for patients infected with an unknown species (46.7%) or two or more species (53.2%). In conclusion, this study expands the current knowledge of the epidemiology and outcomes of invasive candidiasis caused by non-albicans species of Candida in North America. The variability in species distribution in these centers underscores the importance of local epidemiology in guiding the selection of antifungal therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".